Systems and methods for improving magnetic resonance imaging using deep learning

ABSTRACT

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition scheme for imaging a subject using a medical imaging apparatus; acquiring a medical image of the subject according to the accelerated image acquisition scheme using the medical imaging apparatus; applying a deep network model to the medical image to improve the quality of the medical image; and outputting an improved quality image of the subject, for analysis by a physician.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation Application of U.S. application Ser.No. 17/504,725, filed Oct. 19, 2021, which is a Continuation Applicationof U.S. application Ser. No. 17/069,036, filed on Oct. 13, 2020, nowU.S. Pat. No. 11,182,878, issued Nov. 23, 2021 which is a ContinuationApplication of International Patent Application No. PCT/US2019/027826,filed on Apr. 17, 2019, which claims priority to U.S. ProvisionalApplication No. 62/659,837 filed on Apr. 19, 2018, each of which isincorporated herein by reference in its entirety.

BACKGROUND

Magnetic Resonance Imaging (MRI), or nuclear magnetic resonance imaging,is a medical imaging technique commonly used to visualize a subject(e.g., patient) particularly the detailed internal structures in thebody. MRI provides clinical image with improved resolution, highcontrast between the different soft tissues of the body, withoutinvolving ionizing radiation, and is therefore an ideal imaging modalityfor many challenging diseases. Compared with other modalities such asX-ray, CT and ultrasound, MRI takes longer time, sometimes severalminutes, for data acquisition to generate clinically useful images.Undesirable imaging artifacts may appear due to the long scan time. Suchlong scan time for MR exams may result in high imaging cost and limitthe patient volume and accessibility. Some MR applications (e.g.,diffusion-weighted imaging) require the repetition of the same orsimilar acquisition for multiple times in order to achieve adequatesignal-to-noise ratio (SNR).

Methods such as parallel imaging and compressed sensing, have beenemployed for accelerated MR image acquisition, however the practicalacceleration capability is still limited. For example, when the scantime is significantly shortened, parallel imaging suffers from aliasingartifact along with dramatically amplified noise. In another example,compressed sensing suffers from image blurring. Conventional methods mayachieve accelerated data acquisition by: (1) reducing number ofrepetitions, (2) undersampling beyond the Nyquist sampling rate, or (3)reducing image resolution. Such methods may result in images withvarious artifacts such as low SNR, aliasing or blurring.

The term “repetition,” as used herein, generally refers to repetition ofimage acquisitions using the same imaging parameters on the samesubject, repetition of image acquisitions using varied imagingparameters on the same subject, repetition of image acquisitions on asubject from varied angles or the like, thereby achieving enhanced imagequality. For instance, in Arterial Spin Labeling (ASL) MRI, there can bemulti-delay ASL that the high quality images may be computed usingcertain model based or weighted average of multiple images acquiredusing the same imaging parameters but not the same delay parameters. Inanother example, a COSMOS method may be used for achieving high qualityimage in Quantitative Susceptibility Mapping (QSM) MRI, The method ismodel-based or weighted average of multiple images acquired using thesame imaging parameters. During the repeated image acquisition, thesubject may be imaged from different angles (e.g., rotate or move theirhead to various positions) using the same imaging parameters.

One of the conventional methods is Multi-NEX (number of excitations)acquisition, which is referred to the method of repeating the same orsimilar acquisition multiple times to improve SNR for MRI. Define m asthe ground truth image, m_(i) as the acquired image for the i-thacquisition, and n_(i) as the corresponding noise or offsets from theground-truth in m_(i). Then,

m _(i) =m+n _(i),

Typically, the average, including linear averaging or weighted averagingthat possibly based on certain weighting models, of all acquired imagesm_(ave) has higher SNR than any individual image m_(i), and it isconsidered to be an estimate of m. Alternatively, an image denoisingmethod can be used to improve the SNR of m_(i). This process can berepresented by,

{tilde over (m)}=ƒ(m _(i)),

where ƒ represents a denoising function, and the denoised image {tildeover (m)} is the estimate of m. However, this approach has not been aswidely used in the past as simple averaging for most multi-NEXacquisitions.

Parallel imaging and compressed sensing are two popular conventionalmethods for accelerating MR acquisitions by sampling beyond the Nyquistsampling rate. Parallel imaging uses a set of coil arrays with differentcoil sensitivity to synthesize un-acquired data, while compressedsensing utilizes a sparsity constraint and obtains an estimate of theunderlying image by solving an optimization problem. Commonly, parallelimaging and compressed sensing are combined to achieve better imagequality and acceleration capability. Define m_(u) as the image from theundersampled acquisition, then both parallel imaging and compressedsensing can be formulated as:

{tilde over (m)}=ƒ(m _(u)),

where ƒ represents the corresponding image reconstruction, and {tildeover (m)} is the estimated reconstruction. However, such methods mayachieve better image quality at the expense of hardware infrastructuresor acquisition time.

Super resolution is another conventional method for image resolutionimprovement: the original image m_(LR) is acquired with low resolution,and the reconstructed image m_(SR) is with better image resolution.m_(SR) can be obtained by increasing the matrix size of m_(LR) andestimating the additional high spatial frequency contents that have notbeen acquired. Since low resolution images require less acquisitiontime, the super resolution method can also shorten MR scan time.

Similar to the previous formulations, the super resolutionreconstruction can also be represented by a function ƒ that transforms alow resolution image to a high resolution image {tilde over (m)}.

{tilde over (m)}=ƒ(m _(LR)),

The major challenge for the super resolution method is that theun-acquired high spatial frequency information (or function ƒ) isdifficult to estimate directly. Thus, a need exists for an improvedsystem for MR imaging.

SUMMARY

The present disclosure provides improved Magnetic Resonance Imaging(MRI) systems that can address various drawbacks of conventionalsystems, including those recognized above. Method and system of thepresenting disclosure provide improved image quality with shortenedimage acquisition time. The computation time for image reconstruction inruntime may also be reduced compared to the standard iterativereconstruction methods. The provided method and system may significantlyreduce MR scan time by applying deep learning techniques for imagereconstruction so as to enhance image quality. Examples low quality inmedical imaging may include noise (e.g., low signal noise ratio), blur(e.g., motion artifact), shading (e.g., blockage or interference withsensing), missing information (e.g., missing pixels or voxels inpainting due to removal of information or masking), reconstruction(e.g., degradation in the measurement domain), and/or under-samplingartifacts (e.g., under-sampling due to compressed sensing, aliasing).Methods and systems of the present disclosure can be applied to existingsystems seamlessly without a need of a change of the underlyinginfrastructure. In particular, the provided methods and systems mayimprove MR image quality at no additional cost of hardware component andcan be deployed regardless of the configuration or specification of theunderlying infrastructure.

In an aspect of the invention, a computer-implemented method is providedfor improving image quality with shortened acquisition time. The methodcomprises: determining an accelerated image acquisition scheme forimaging a subject using a medical imaging apparatus; acquiring, usingthe medical imaging apparatus, a medical image of the subject accordingto the accelerated image acquisition scheme; applying a deep networkmodel to the medical image to improve the quality of the medical image;and outputting an improved quality image of the subject for analysis bya physician. In some embodiments, the medical image includes a magneticresonance image.

In some embodiments of the invention, determining the accelerated imageacquisition scheme comprises: receiving a target acceleration factor ortarget acquisition speed via a graphical user interface, and selectingfrom a plurality of accelerated image acquisition schemes based on thetarget acceleration factor or the target acquisition speed. In somecases, the accelerated image acquisition scheme is selected by applyingthe plurality of accelerated image acquisition schemes to a portion ofthe medical image for simulation.

In some embodiments, the accelerated image acquisition scheme isdetermined based on user input and real-time simulated output images. Insome embodiments, the accelerated image acquisition scheme comprises oneor more parameters related to an undersampled k-space, an undersamplingpattern, and a reduced number of repetitions. In some cases, theundersampling pattern is selected from a group consisting of a uniformundersampling pattern, a random undersampling pattern, and a variableundersampling pattern. In some embodiments, the medical image comprisesundersampled k-space image or image acquired using reduced number ofrepetitions.

In some embodiments, the deep learning model is trained with adaptivelyoptimized metrics based on user input and real-time simulated outputimages. In some embodiments, the deep learning model is trained usingtraining datasets comprising at least a low quality image and a highquality image. In some cases, the low quality image is generated byapplying one or more filters or adding synthetic noise to the highquality image to create noise or undersampling artifacts. In someembodiments, the deep learning model is trained using image patches thatcomprise a portion of at least a low quality image and a high qualityimage. In some cases, the image patches are selected based on one ormore metrics quantifying an image similarity.

In some embodiments, the deep learning model is a deep residual learningmodel. In some embodiments, the deep learning model is trained byadaptively tuning one or more model parameters to approximate areference image. In some embodiments, the improved quality image of thesubject has greater SNR, higher resolution, or less aliasing comparedwith the medical image acquired using the medical imaging apparatus.

Another aspect of the present disclosure provides a non-transitorycomputer readable medium comprising machine executable code that, uponexecution by one or more computer processors, implements any of themethods above or elsewhere herein. For example, the one or moreprocessors may perform operations that comprise: determining anaccelerated image acquisition scheme for imaging a subject using amedical imaging apparatus; acquiring, using the medical imagingapparatus, a medical image of the subject according to the acceleratedimage acquisition scheme; applying a deep network model to the medicalimage to improve the quality of the medical image; and outputting animproved quality image of the subject for analysis by a physician.

In some embodiments, the medical image includes a magnetic resonanceimage. In some embodiments, the accelerated image acquisition scheme isdetermined based on user input and real-time simulated output images. Insome embodiments, the accelerated image acquisition scheme comprises oneor more parameters related to an undersampled k-space, an undersamplingpattern, and a reduced number of repetitions.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and descriptions are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “figure” and “FIG.” herein) of which:

FIG. 1 schematically illustrates an example of transforming low qualityimage to high quality image using a deep learning algorithm;

FIG. 2 schematically illustrates a magnetic resonance imaging (MRI)system in which an imaging accelerator of the presenting disclosure maybe implemented;

FIG. 3 shows a block diagram of an example of a MR imaging acceleratorsystem, in accordance with embodiments of the present disclosure;

FIG. 4 shows examples of determining an acquisition scheme using aninteractive MRI acquisition module; and

FIG. 5 illustrates an example of method for improving MR image qualitywith accelerated acquisition.

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

Accelerated Acquisition

The term “accelerated acquisition,” as used herein, generally refers toshortened MR acquisition time. The provided system and method may beable to achieve MR imaging with improved quality by an accelerationfactor of at least 1.5, 2, 3, 4, 5, 10, 15, 20, a factor of a valueabove 20 or below 1.5, or a value between any two of the aforementionedvalues. An accelerated acquisition can be achieved via approaches suchas: (1) reducing the number of repetitions for multi-NEX acquisition,(2) reducing the sampling rate below the Nyquist rate, or (3) reducingthe image resolution. An acceleration scheme may comprise using one ormore of the above approaches. An acceleration scheme may comprise usingany combination of the above approaches. In an example, the acceleratedacquisition may be achieved by reducing the number of repetitions. Inanother example, the accelerated acquisition may be achieved byundersampling the k-space. In a further example, the acceleratedacquisition may be achieved by a combination of reducing the number ofrepetition and undersampling the k-space. An acceleration scheme mayalso comprise one or more parameters that may affect an accelerationresult of a selected approach. An acceleration scheme may also bereferred to as acquisition scheme or accelerated image acquisitionscheme which are used interchangeably throughout the specification.

Image formation in MR imaging is based on the traversal of k-space intwo or three dimensions in a manner determined by the pulse sequence.Although acquisition of data in the frequency-encoding direction istypically rapid and on the order of several milliseconds, a separateecho collected with a slightly different value of the appliedphase-encoding gradient is required to sample each value of k_(y) alongthe phase-encoding axis. The sampling of k-space through a prescribednumber of phase-encoding steps therefore accounts for the majority ofthe acquisition time in most MR imaging acquisitions.

In some cases, the accelerated data acquisition may be achieved byundersampling the k-space. The k-space can be undersampled according tovarious sampling schemes. The sampling scheme may include at least asampling density along a given direction, or a predefinedpattern/trajectory. For example, k-space may be undersampled least alonga given direction, by virtue of the density of samples relative to theNyquist criterion for the intended image's FOV (field of view) of atleast 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% and the like.The sampling scheme may comprise various other factors such asspecifying a region of k-space is undersampled, oversampled orcritically sampled. In some embodiments, one or more parameters relatedto a sampling scheme may be specified in an acceleration scheme.

In some embodiments, an undersampling pattern for acceleratedacquisition may be specified in an acceleration scheme. The acceleratedacquisition may use an undersampling pattern such as uniformundersampling patterns, random undersampling patterns, or variableundersampling patterns. Various patterns or trajectories such as spiralsampling pattern, radially arranged strips, rectilinear pattern,Poisson-disc, jittered grid or randomized pattern may be applied forsampling k-space. The pattern or trajectory may be determined accordingto a specific imaging technique. For example, to achieve a betterparallel reconstruction, the sampling pattern should not containfrequently occurring large gaps. Therefore, Poisson-disc and jitteredgrid with uniform or variable sampling density may be selected assampling patterns for parallel processing.

As aforementioned, images acquired under shortened acquisition time mayexperience various artifacts. Such images may have lower quality such aslow SNR, blurring or aliasing. Methods and systems of the presentdisclosure may mitigate these artifacts by applying a machine learningmethod to the low quality images resulting in high quality MR image withaccelerated acquisition. Such method may be used for imagereconstruction and can be used in combination with any existing MRtechniques.

Deep Learning Method

FIG. 1 schematically illustrates an example of transforming low qualityimage 101, 103 to high quality image 105 using deep learning algorithm110. The low quality image may be acquired using accelerated dataacquisition approaches as described above. In some cases, theaccelerated data acquisition approach may be specified in an acquisitionscheme. Define m_(acc) as the image corresponding to the accelerateddata acquisition. An example of accelerated 2D acquisition with reducedsampling rate and/or reduced image resolution is shown in FIG. 1 .During the image reconstruction, a deep learning algorithm may beapplied to the low quality image to estimate a function ƒ thattransforms the low quality image m_(acc) to a high quality image {tildeover (m)}. The high quality image may be high SNR, alias-free, or highresolution image. In some cases, this function ƒ may be obtained byoptimizing metrics g between the ground truth image m and the estimatedimage {tilde over (m)} through a training process on a number oftraining datasets:

min Σg _(i)(k(m),k({tilde over (m)})),

st.{tilde over (m)}=ƒ(m _(acc))

There can be one or more cost metrics which can be combined withoptimized weightings. g can be any suitable metrics such as I₂ norm∥k(m)−k({tilde over (m)})∥², I₁ norm ∥k(m)−k(m)∥₁, structuraldissimilarity or other metrics. In some cases, k can be identitytransform then the metrics are calculated in the image domain. k can beany other transforms, such as Fourier transform, therefore the metricsmay be calculated in the corresponding frequency domain. In some cases,the g metric may be used as criteria during the training process of thedeep learning model. In some cases, the g metrics can also be a networkmodel that is separately or simultaneously trained together with ƒ, todiscriminate image states and evaluate image quality. In some cases, thedeep learning model may be trained with adaptively optimized metricsbased on user input and real-time simulated output images.

The machine learning method 110 may comprise one or more machinelearning algorithms. The artificial neural network may employ any typeof neural network model, such as a feedforward neural network, radialbasis function network, recurrent neural network, convolutional neuralnetwork, deep residual learning network and the like. In someembodiments, the machine learning algorithm may comprise a deep learningalgorithm such as convolutional neural network (CNN). Examples ofmachine learning algorithms may include a support vector machine (SVM),a naïve Bayes classification, a random forest, a deep learning modelsuch as neural network, or other supervised learning algorithm orunsupervised learning algorithm. In some cases, the method may be asupervised deep machine learning method.

The deep learning network such as CNN may comprise multiple layers. Forexample, the CNN model may comprise at least an input layer, a number ofhidden layers and an output layer. A CNN model may comprise any totalnumber of layers, and any number of hidden layers. The simplestarchitecture of a neural network starts with an input layer followed bya sequence of intermediate or hidden layers, and ends with output layer.The hidden or intermediate layers may act as learnable featureextractors, while the output layer in this example provides MR imageswith improved quality.

Each layer of the neural network may comprise a number of neurons (ornodes). A neuron receives input that comes either directly from theinput data (e.g., low quality image data, undersampled k-space data,etc.) or the output of other neurons, and performs a specific operation,e.g., summation. In some cases, a connection from an input to a neuronis associated with a weight (or weighting factor). In some cases, theneuron may sum up the products of all pairs of inputs and theirassociated weights. In some cases, the weighted sum is offset with abias. In some cases, the output of a neuron may be gated using athreshold or activation function. The activation function may be linearor non-linear. The activation function may be, for example, a rectifiedlinear unit (ReLU) activation function or other functions such assaturating hyperbolic tangent, identity, binary step, logistic, arcTan,softsign, parameteric rectified linear unit, exponential linear unit,softPlus, bent identity, softExponential, Sinusoid, Sinc, Gaussian,sigmoid functions, or any combination thereof.

In some embodiments, the training process of the deep learning model mayemploy a residual learning method. In some instances, the residuallearning framework may be used for evaluating a trained model. In someinstances, the residual learning framework with skip connections maygenerate estimated ground-truth images from the lower quality imagessuch as complex-valued aliased ones, with refinement to ensure it isconsistent with measurement (data consistency). The lower quality inputimage can be simply obtained via inverse Fourier Transform (FT) ofundersampled data. In some cases, what the model learns is the residualof the difference between the raw image data and ground-truth imagedata, which is sparser and less complex to approximate using the networkstructure. The method may use by-pass connections to enable the residuallearning. In some cases, a residual network may be used and the directmodel output may be the estimated residual/error between low-quality andhigh quality images. In other word, the function to be learned by thedeep learning framework is a residual function which in some situationsmay be easy to optimize. The higher quality image can be recovered byadding the low quality image to the residual. This residual trainingapproach may reduce the complexity of training and achieve betterperformance, where the output level is small, reducing the likelihood ofintroducing large image artifacts even when the model does not predictperfectly.

The training datasets may be input to a deep network comprising residuallearning and a convolutional neural network. The model may be trainedusing a reference image of target quality that has a relatively high SNRand better resolution. In some cases, the deep learning model may betrained with adaptively tuned parameters based on user input andreal-time simulated output images. Alternatively or in addition to, thedeep learning network may be a “plain” CNN that does not involveresidual learning. In some cases, a type of deep learning network may beselected based on the goal of the MR image enhancement, image datacharacteristics or other factors. For example, according to differentgoal of image enhancement such as to improve SNR, to achieve alias-free,or to increase resolution image, a deep residual learning network or aplain CNN may be selected. In some cases, during the training process,the deep learning model may adaptively tune model parameters toapproximate the reference image of target quality from an initial set ofthe input images, and outputting an improved quality image.

In some embodiments, the training process of the deep learning model mayemploy a patch-based approach. In some cases, the image used fortraining (e.g., low quality and high quality images) may be divided intopatches. For example, a pair of training images such as a pair of highquality image and lower quality image may each be divided spatially intoa set of smaller patches. The high quality image and the lower qualityimage can be divided into a set of patches. A size of an image patch maybe dependent on the application such as the possible size a recognizablefeature contained in the image. Alternatively, the size of an imagepatch may be pre-determined or based on empirical data.

In some cases, one or more patches may be selected from the set ofpatches and used for training the model. In some instances, one or morepatches corresponding to the same coordinates may be selected from apair of images. Alternatively, a pair of patches may not correspond tothe same coordinates. The selected pair of patches may then be used fortraining. In some cases, patches from the pair of images with similarityabove a pre-determined threshold are selected. One or more pairs ofpatches may be selected using any suitable metrics quantifying imagesimilarity. For instance, one or more pairs of patches may be selectedby calculating a structural similarity index, peak signal-to-noise ratio(PSNR), mean squared error (MSE), absolute error, other metrics or anycombination of the above. In some cases, the similarity comparison maybe performed using sliding window over the image.

The deep learning model 110 may be trained using one or more trainingdatasets comprising the MR image data. In an example, the trainingdataset may be 3D volume image data comprising multiple axial slices,and each slice may be complex-valued images each may include twochannels for real and imaginary components. The training dataset maycomprise lower quality images obtained from MR imaging devices. Forexample, the low quality input image can be simply obtained via inverseFourier Transform (FT) of undersampled data (e.g., k-space data). Insome cases, the training dataset may comprise augmented datasetsobtained from simulation. For instance, image data from clinicaldatabase may be used to generate low quality image data. In an example,FFT and filters may be applied to raw image data to transform it to lowquality image data such as by applying masks to remove certain datapoints so as to create artifacts. In another example, image blurringfilters may be applied directly on the high quality images to generatelow quality images. In a further example, synthetic noise may be addedto high quality images to create noisy images. In some embodiments, thehigher quality input image data may be obtained from direct imageacquisition using an MR imaging device with longer acquisition time orrepeated image acquisitions as described elsewhere herein.

The trained deep learning model may be used for transforming input datacomprising lower quality MR image data to output data comprising higherquality MR image data. In some cases, the input data may be 3D volumecomprising multiple axial slices. In an example, an input and outputslices may be complex-valued images of the same size and each includetwo channels for real and imaginary components. With aid of the providedsystem, higher quality MR image may be obtained with acceleratedacquisition.

In some embodiments, during the training phase additional imageprocessing steps can be applied to the deep learning input images basedon users' preference. For example, image filters such as high passfilter, low pass filter and the like can be applied to the input images.In some cases, synthetic noise may be added to the input images.Similarly, post image processing steps can be applied to the deeplearning output images based on users' preference. For example, imagefilters such as high pass filter, low pass filter and the like can beapplied to the output images. In some cases, synthetic noise may beadded to the output images.

Systems and methods of the present disclosure may provide an imagingaccelerator system can be implemented on any existing MR imaging systemwithout a need of a change of hardware infrastructure. The imagingaccelerator system may be implemented in software, hardware, firmware,embedded hardware, standalone hardware, application specific-hardware,or any combination of these. The imaging accelerator system can be astandalone system that is separate from the MR imaging system.Alternatively or in addition to, the imaging accelerator system can beintegral to the MR imaging system such as a component of a controller ofthe MR imaging system.

System Overview

FIG. 2 schematically illustrates a magnetic resonance imaging (MRI)system 200 in which an imaging accelerator 240 of the presentingdisclosure may be implemented. The MRI system 200 may comprise a magnetsystem 203, a patient transport table 205 connected to the magnetsystem, and a controller 201 operably coupled to the magnet system. Inone example, a patient may lie on the patient transport table 205 andthe magnet system 203 would pass around the patient. The controller 201may control magnetic fields and radio frequency (RF) signals provided bythe magnet system 203 and may receive signals from detectors in themagnet system 203. The MRI system 200 may further comprise a computersystem 210 and one or more databases operably coupled to the controller201 over the network 230. The computer system 210 may be used forimplementing the MR imaging accelerator 240. The computer system 210 maybe used for generating an imaging accelerator using training datasets.Although the illustrated diagram shows the controller and computersystem as separate components, the controller and computer system can beintegrated into a single component.

The controller 201 may be operated to provide the MRI sequencecontroller information about a pulse sequence and/or to manage theoperations of the entire system, according to installed softwareprograms. The controller may also serve as an element for instructing apatient to perform tasks, such as, for example, a breath hold by a voicemessage produced using an automatic voice synthesis technique. Thecontroller may receive commands from an operator which indicate the scansequence to be performed. The controller may comprise various componentssuch as a pulse generator module which is configured to operate thesystem components to carry out the desired scan sequence, producing datathat indicate the timing, strength and shape of the RF pulses to beproduced, and the timing of and length of the data acquisition window.Pulse generator module may be coupled to a set of gradient amplifiers tocontrol the timing and shape of the gradient pulses to be producedduring the scan. Pulse generator module also receives patient data froma physiological acquisition controller that receives signals fromsensors attached to the patient, such as ECG (electrocardiogram) signalsfrom electrodes or respiratory signals from a bellows. Pulse generatormodule may be coupled to a scan room interface circuit which receivessignals from various sensors associated with the condition of thepatient and the magnet system. A patient positioning system may receivecommands through the scan room interface circuit to move the patient tothe desired position for the scan.

The controller 201 may comprise a transceiver module which is configuredto produce pulses which are amplified by an RF amplifier and coupled toRF coil by a transmit/receive switch. The resulting signals radiated bythe excited nuclei in the patient may be sensed by the same RF coil andcoupled through transmit/receive switch to a preamplifier. The amplifiednuclear magnetic resonance (NMR) signals are demodulated, filtered, anddigitized in the receiver section of transceiver. Transmit/receiveswitch is controlled by a signal from pulse generator module toelectrically couple RF amplifier to coil for the transmit mode and topreamplifier for the receive mode. Transmit/receive switch may alsoenable a separate RF coil (for example, a head coil or surface coil, notshown) to be used in either the transmit mode or receive mode.

The NMR signals picked up by RF coil may be digitized by the transceivermodule and transferred to a memory module coupled to the controller. Thereceiver in the transceiver module may preserve the phase of theacquired NMR signals in addition to signal magnitude. The down convertedNMR signal is applied to an analog-to-digital (A/D) converter (notshown) which samples and digitizes the analog NMR signal. The samplesmay be applied to a digital detector and signal processor which producesin-phase (I) values and quadrature (Q) values corresponding to thereceived NMR signal. The resulting stream of digitized I and Q values ofthe received NMR signal may then be employed to reconstruct an image.The provided imaging accelerator may be used for reconstructing theimage to achieve an improved quality.

The controller 201 may comprise or be coupled to an operator console(not shown) which can include input devices (e.g., keyboard) and controlpanel and a display. For example, the controller may have input/output(I/O) ports connected to an I/O device such as a display, keyboard andprinter. In some cases, the operator console may communicate through thenetwork with the computer system 210 that enables an operator to controlthe production and display of images on a screen of display. The imagesmay be MR images with improved quality acquired according to anaccelerated acquisition scheme. The image acquisition scheme may bedetermined automatically by the imaging accelerator 240 and/or by a useras described later herein.

The MRI system 200 may comprise a user interface. The user interface maybe configured to receive user input and output information to a user.The user input may be related to control of image acquisition. The userinput may be related to the operation of the MRI system (e.g., certainthreshold settings for controlling program execution, parameters forcontrolling the joint estimation of coil sensitivity and imagereconstruction, etc). The user input may be related to variousoperations or settings about the imaging accelerator. The user input mayinclude, for example, a selection of a target location, displayingsettings of a reconstructed image, customizable display preferences,selection of an acquisition scheme, settings of a selected acquisitionscheme, and various others. The user interface may include a screen suchas a touch screen and any other user interactive external device such ashandheld controller, mouse, joystick, keyboard, trackball, touchpad,button, verbal commands, gesture-recognition, attitude sensor, thermalsensor, touch-capacitive sensors, foot switch, or any other device.

The MRI platform 200 may comprise computer systems 210 and databasesystems 220, which may interact with the controller. The computer systemcan comprise a laptop computer, a desktop computer, a central server,distributed computing system, etc. The processor may be a hardwareprocessor such as a central processing unit (CPU), a graphic processingunit (GPU), a general-purpose processing unit, which can be a singlecore or multi core processor, a plurality of processors for parallelprocessing, in the form of fine-grained spatial architectures such as afield programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), and/or one or more Advanced RISC Machine (ARM)processors. The processor can be any suitable integrated circuits, suchas computing platforms or microprocessors, logic devices and the like.Although the disclosure is described with reference to a processor,other types of integrated circuits and logic devices are alsoapplicable. The processors or machines may not be limited by the dataoperation capabilities. The processors or machines may perform 512 bit,256 bit, 128 bit, 64 bit, 32 bit, or 16 bit data operations. Detailsregarding the computer system are described with respect to FIG. 3 .

The MRI system 200 may comprise one or more databases. The one or moredatabases 220 may utilize any suitable database techniques. Forinstance, structured query language (SQL) or “NoSQL” database may beutilized for storing MR image data, raw image data, reconstructed imagedata, training datasets, trained model, parameters of an acquisitionscheme, etc. Some of the databases may be implemented using variousstandard data-structures, such as an array, hash, (linked) list, struct,structured text file (e.g., XML), table, JSON, NOSQL and/or the like.Such data-structures may be stored in memory and/or in (structured)files. In another alternative, an object-oriented database may be used.Object databases can include a number of object collections that aregrouped and/or linked together by common attributes; they may be relatedto other object collections by some common attributes. Object-orienteddatabases perform similarly to relational databases with the exceptionthat objects are not just pieces of data but may have other types offunctionality encapsulated within a given object. If the database of thepresent disclosure is implemented as a data-structure, the use of thedatabase of the present disclosure may be integrated into anothercomponent such as the component of the present invention. Also, thedatabase may be implemented as a mix of data structures, objects, andrelational structures. Databases may be consolidated and/or distributedin variations through standard data processing techniques. Portions ofdatabases, e.g., tables, may be exported and/or imported and thusdecentralized and/or integrated.

The network 230 may establish connections among the components in theMRI platform and a connection of the MRI system to external systems. Thenetwork 230 may comprise any combination of local area and/or wide areanetworks using both wireless and/or wired communication systems. Forexample, the network 230 may include the Internet, as well as mobiletelephone networks. In one embodiment, the network 230 uses standardcommunications technologies and/or protocols. Hence, the network 230 mayinclude links using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 2G/3G/4G mobilecommunications protocols, asynchronous transfer mode (ATM), InfiniBand,PCI Express Advanced Switching, etc. Other networking protocols used onthe network 230 can include multiprotocol label switching (MPLS), thetransmission control protocol/Internet protocol (TCP/IP), the UserDatagram Protocol (UDP), the hypertext transport protocol (HTTP), thesimple mail transfer protocol (SMTP), the file transfer protocol (FTP),and the like. The data exchanged over the network can be representedusing technologies and/or formats including image data in binary form(e.g., Portable Networks Graphics (PNG)), the hypertext markup language(HTML), the extensible markup language (XML), etc. In addition, all orsome of links can be encrypted using conventional encryptiontechnologies such as secure sockets layers (SSL), transport layersecurity (TLS), Internet Protocol security (IPsec), etc. In anotherembodiment, the entities on the network can use custom and/or dedicateddata communications technologies instead of, or in addition to, the onesdescribed above.

Acquisition Scheme

In some embodiments, the MR imaging accelerator of the presentingdisclosure may enable accelerated image acquisition with improved imagequality. In some cases, an acquisition scheme may be automaticallyselected and/or determined by the imaging accelerator. In some cases, anacquisition scheme may be selected or defined by a user. One or moreparameters of an acquisition scheme may include, for example, the numberof encoding steps, the k-space sampling pattern, image resolution,field-of-view, scanning speed, sampling schemes such as pattern, fullysampled regions, undersampled, regions, and various others. In somecases, the acquisition scheme may also include selecting areconstruction method or setting one or more parameters related to areconstruction method.

Imaging Accelerator System

FIG. 3 shows a block diagram of an example of a MR imaging acceleratorsystem 300, in accordance with embodiments of the present disclosure.The MR imaging accelerator system 300 may comprise an MR imagingaccelerator 240 which can be the same as the imaging accelerator asdescribed in FIG. 2 . The MR imaging accelerator 240 may comprisemultiple components, including but not limited to, an acceleratortraining module 302, an image reconstruction module 304, an interactiveMRI acquisition module 306 and a user interface module 308.

The accelerator training module 302 may be configured to obtain andmanage training datasets. The accelerator training module 302 maycomprise a deep learning algorithm such as convolutional neural network(CNN). The accelerator training module may be configured to implementthe machine learning methods as described above. The acceleratortraining module may train a model off-line. Alternatively oradditionally, the accelerator training module may use real-time data asfeedback to refine the model.

The image reconstruction module 304 may be configured to reconstructimages using a trained model obtained from the accelerator trainingmodule. The image reconstruction module may take one or more k-spaceimages or lower quality MR image data as input and output MR image datawith improved quality.

The interactive MRI acquisition module 306 may be operably coupled tothe image reconstruction module and/or the controller of the MRI system.The interactive MRI acquisition module 306 may be configured to generatean acquisition scheme. In some cases, the interactive MRI acquisitionmodule may receive a user input indicating a desired acceleration (e.g.,acceleration factor, acquisition speed, image resolution, field of view,target region, etc). In response to receiving the target or desiredacceleration, the interactive MRI acquisition module may run tests onone or more acquisition schemes and determine an optimal acquisitionscheme. The optimal acquisition scheme may be determined based on apredetermined rule. For instance, the optimal acquisition scheme may bedetermined based on the quality of the output image. For example, anacquisition scheme meeting the target acceleration goal while providingthe best quality images may be selected. In some case, the interactiveMRI acquisition module may allow a user to define an acquisition scheme.In response to receiving a user defined acquisition scheme, theinteractive MRI acquisition module may run simulations and generateoutput images associated with the acquisition scheme. A user may or maynot further adjust the acquisition scheme so as to change the quality orother characteristics of the output images. The determined acquisitionscheme may then be transmitted to the controller of the MRI system forcontrolling the operation of the imaging system as described elsewhereherein. The interactive MRI acquisition module may be operably coupledto the user interface module 308 for receiving user input and outputtingan auto-generated acquisition scheme or simulated images.

The user interface module 308 may render a graphical user interface(GUI) 340 allowing a user to select an acquisition scheme, modify one ormore parameters of an acquisition scheme, viewing information related toimaging and acquisition settings and the like. The GUI may showgraphical elements that permit a user to view or access informationrelated to image acquisition. A graphical user interface can havevarious interactive elements such as buttons, text boxes and the like,which may allow a user to provide input commands or contents by directlytyping, clicking or dragging such interactive elements. For example, auser may manually create or modify a scanning pattern, select anacceleration factor and set other parameters via the GUI. Furtherdetails are described later herein with respect to FIG. 4 .

In some cases, the graphical user interface (GUI) or user interface maybe provided on a display 335. The display may or may not be atouchscreen. The display may be a light-emitting diode (LED) screen,organic light-emitting diode (OLED) screen, liquid crystal display (LCD)screen, plasma screen, or any other type of screen. The display may beconfigured to show a user interface (UI) or a graphical user interface(GUI) rendered through an application (e.g., via an applicationprogramming interface (API) executed on the local computer system or onthe cloud).

The imaging accelerator system 300 may be implemented in software,hardware, firmware, embedded hardware, standalone hardware, applicationspecific-hardware, or any combination of these. The imaging acceleratorsystem, modules, components, algorithms and techniques may includeimplementation in one or more computer programs that are executableand/or interpretable on a programmable system including at least oneprogrammable processor, which may be special or general purpose, coupledto receive data and instructions from, and to transmit data andinstructions to, a storage system, at least one input device, and atleast one output device. These computer programs (also known asprograms, software, software applications, or code) may include machineinstructions for a programmable processor, and may be implemented in ahigh-level procedural and/or object-oriented programming language,and/or in assembly/machine language. As used herein, the terms“machine-readable medium” and “computer-readable medium” refer to anycomputer program product, apparatus, and/or device (such as magneticdiscs, optical disks, memory, or Programmable Logic Devices (PLDs)) usedto provide machine instructions and/or data to a programmable processor.The imaging accelerator system can be a standalone system that isseparate from the MR imaging system. Alternatively or in addition to,the imaging accelerator system can be integral to the MR imaging systemsuch as a component of a controller of the MR imaging system.

In some cases, the imaging accelerator system may employ an edgeintelligence paradigm that data processing and MR image enhancement isperformed at the edge or edge gateway (MRI system). In some instances,machine learning model may be built, developed and trained on acloud/data center and run on the MRI system (e.g., hardwareaccelerator). For example, software that run on the edge may be theimage reconstruction module 304. Software that run on the cloud or anon-premises environment may be the accelerator training module fortraining, developing, and managing the deep learning models or theinteractive MRI acquisition module 306 to remotely configure the MRIcontroller.

FIG. 4 shows examples of determining an acquisition scheme via theaforementioned interactive MRI acquisition module. An acquisition schememay be determined autonomously, semi-autonomously or manually. In afully automated mode 400, the imaging accelerator may be configured toautomatically determine an optimal acquisition scheme. For example, auser may input, via a user interface, a target acceleration. The targetacceleration may be provided via any suitable formats on theaforementioned GUI, such as a selection from drop-down menu,manipulating a graphical element (e.g., slider bar), direct input in atext box (e.g., input an acceleration factor) or via other suitablemeans such as voice command and the like. The acceleration may berelated to an aspect of image acquisition, including but not limited to,acceleration factor, acquisition speed, image resolution, field of view,and target region. In an example, the target acceleration may be aselection from ‘fast acquisition’, ‘mid acquisition’, ‘slowacquisition.’ In another example, the target acceleration may be anacceleration factor such as a factor of 1.5, 2, 3, 4, 5, 10, 15, 16, 17,18, 19, 20, a factor of a value above 20 or below 1.5, or a valuebetween any two of the aforementioned values.

In some embodiments, in response to receiving the target acceleration, asimulation of one or more acquisition schemes may be performed in orderto determine an optimal acquisition scheme. In some cases, the one ormore acquisition schemes may be applied to image patches to increasecomputation speed in the simulation. The optimal acquisition scheme maybe determined based on a predetermined rule. For instance, the optimalacquisition scheme may be determined based on the quality of the outputimage (patch). For example, an acquisition scheme meeting the targetacceleration goal while providing the best quality images may beselected. In some cases, the determined acquisition scheme may bedisplayed to a user for further approval or further adjustment. Theapproved or determined acquisition scheme may be transmitted to thecontroller of the MRI system for controlling the imaging operation ofthe imaging system consistent with the disclosure herein.

In some case, a user may be allowed to define an acquisition scheme in asemi-autonomous fashion 410. A user may specify one or more parametersof an acquisition scheme. In response to receiving the acquisitionscheme, the interactive MRI acquisition module may run simulations andoutput images associated with the acquisition scheme. A user may or maynot further adjust the acquisition scheme so as to change the quality orother characteristics of the output images. In some instances, a usermay be provided with system advised adjustment. In some instances, auser may manually adjust one or more parameters upon viewing thesimulated output images on a display. In the illustrated example 420, auser may be presented a lower quality image (left) and a simulatedhigher quality image (right) that can be achieved under the currentacquisition scheme. In some cases, the simulated image may bedynamically updated while the user adjusting one or more parameters ofthe acquisition scheme. The determined acquisition scheme may then betransmitted to the controller of the MRI system for controlling theoperations of the imaging system as described elsewhere herein.

The present disclosure provides computer systems that are programmed toimplement methods of the disclosure. Referring back to FIG. 3 , acomputer system 300 is programmed or otherwise configured to manageand/or implement an MR imaging accelerator and its operations. Thecomputer system 300 can regulate various aspects of FIGS. 1-2 of thepresent disclosure, such as, for example, the magnetic system,accelerator training module, the image reconstruction module, theinteractive MRI acquisition module, the user interface module, and themethods illustrated in FIG. 4 and FIG. 5 .

The computer system 300 may include a central processing unit (CPU, also“processor” and “computer processor” herein), a graphic processing unit(GPU), a general-purpose processing unit, which can be a single core ormulti core processor, or a plurality of processors for parallelprocessing. The computer system 300 can also include memory or memorylocation (e.g., random-access memory, read-only memory, flash memory),electronic storage unit (e.g., hard disk), communication interface(e.g., network adapter) for communicating with one or more othersystems, and peripheral devices 335, 220, such as cache, other memory,data storage and/or electronic display adapters. The memory, storageunit, interface and peripheral devices are in communication with the CPUthrough a communication bus (solid lines), such as a motherboard. Thestorage unit can be a data storage unit (or data repository) for storingdata. The computer system 300 can be operatively coupled to a computernetwork (“network”) 230 with the aid of the communication interface. Thenetwork 230 can be the Internet, an internet and/or extranet, or anintranet and/or extranet that is in communication with the Internet. Thenetwork 230 in some cases is a telecommunication and/or data network.The network 230 can include one or more computer servers, which canenable distributed computing, such as cloud computing. The network 230,in some cases with the aid of the computer system 300, can implement apeer-to-peer network, which may enable devices coupled to the computersystem 300 to behave as a client or a server.

The CPU can execute a sequence of machine-readable instructions, whichcan be embodied in a program or software. The instructions may be storedin a memory location, such as the memory. The instructions can bedirected to the CPU, which can subsequently program or otherwiseconfigure the CPU to implement methods of the present disclosure.Examples of operations performed by the CPU can include fetch, decode,execute, and writeback.

The CPU can be part of a circuit, such as an integrated circuit. One ormore other components of the system can be included in the circuit. Insome cases, the circuit is an application specific integrated circuit(ASIC).

The storage unit can store files, such as drivers, libraries and savedprograms. The storage unit can store user data, e.g., user preferencesand user programs. The computer system 300 in some cases can include oneor more additional data storage units that are external to the computersystem, such as located on a remote server that is in communication withthe computer system through an intranet or the Internet.

The computer system 300 can communicate with one or more remote computersystems through the network 230. For instance, the computer system 300can communicate with a remote computer system of a user or aparticipating platform (e.g., operator). Examples of remote computersystems include personal computers (e.g., portable PC), slate or tabletPC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones(e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personaldigital assistants. The user can access the computer system 300 via thenetwork 230.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 300, such as, for example, on the memoryor electronic storage unit. The machine executable or machine readablecode can be provided in the form of software. During use, the code canbe executed by the processor. In some cases, the code can be retrievedfrom the storage unit and stored on the memory for ready access by theprocessor. In some situations, the electronic storage unit can beprecluded, and machine-executable instructions are stored on memory.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 300, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 300 can include or be in communication with anelectronic display 335 that comprises a user interface (UI) 340 forproviding, for example, displaying reconstructed images, acquisitionschemes, for example. Examples of UI's include, without limitation, agraphical user interface (GUI) and web-based user interface. The GUI canbe rendered by the user interface module 308.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit. For example,some embodiments use the algorithm illustrated in FIG. 4 and FIG. 5 orother algorithms provided in the associated descriptions above.

FIG. 5 illustrates an example of method 500 for improving MR imagequality with accelerated acquisition. MR images may be obtained from MRimaging system (operation 510) for training a deep learning model. TheMR images may be used to form training datasets (operation 520). Thetraining dataset may comprise relatively lower quality image data andcorresponding higher quality image data (i.e., ground truth data). Thetraining dataset may comprise low quality images obtained from imagingdevices. For example, the low quality input image can be simply obtainedvia inverse Fourier Transform (FT) of undersampled data (e.g., k-spacedata). The training dataset may comprise augmented datasets obtainedfrom simulation. For instance, image data from clinical database may beused to generate low quality image data. In an example, FFT and filtersmay be applied to raw image data to transform it to low quality imagedata such as by applying masks to remove certain data points so as tocreate artifacts. Similarly, higher quality input image data may beobtained from direct image acquisition with longer acquisition time. Inan example, training dataset may be 3D volume image data comprisingmultiple axial slices, and each slice may be complex-valued images eachmay include two channels for real and imaginary components.

The training step 530 may comprise a deep learning algorithm consistentwith the disclosure herein. The deep learning algorithm may be aconvolutional neural network, for example. In some cases, the deeplearning algorithm may be a deep residual learning network. The trainedaccelerator may then be used for transforming a lower quality MR imageinto a higher quality MR image with a selected acceleration scheme. Theacceleration scheme may be determined by receiving a target accelerationfrom a user (operation 540) then performing simulations on a pluralityof acquisition schemes to determine an optimal acquisition scheme(operation 550). Alternatively or in addition to, the acquisition schememay be determined by receiving a user specified acquisition scheme(operation 540) then generating real-time simulation results (operation570) to show the simulated output images under the acquisition scheme(operation 570). A user may confirm or further adjust the acquisitionscheme upon viewing the simulated output images (operation 580).

Although FIG. 5 shows a method in accordance with some embodiments aperson of ordinary skill in the art will recognize that there are manyadaptations for various embodiments. For example, the operations can beperformed in any order. Some of the operations may be precluded, some ofthe operations may be performed concurrently in one step, some of theoperations repeated, and some of the operations may comprise sub-stepsof other operations.

Whenever the term “at least,” “greater than,” or “greater than or equalto” precedes the first numerical value in a series of two or morenumerical values, the term “at least,” “greater than” or “greater thanor equal to” applies to each of the numerical values in that series ofnumerical values. For example, greater than or equal to 1, 2, or 3 isequivalent to greater than or equal to 1, greater than or equal to 2, orgreater than or equal to 3.

Whenever the term “no more than,” “less than,” or “less than or equalto” precedes the first numerical value in a series of two or morenumerical values, the term “no more than,” “less than,” or “less than orequal to” applies to each of the numerical values in that series ofnumerical values. For example, less than or equal to 3, 2, or 1 isequivalent to less than or equal to 3, less than or equal to 2, or lessthan or equal to 1.

As used herein A and/or B encompasses one or more of A or B, andcombinations thereof such as A and B. It will be understood thatalthough the terms “first,” “second,” “third” etc. are used herein todescribe various elements, components, regions and/or sections, theseelements, components, regions and/or sections should not be limited bythese terms. These terms are merely used to distinguish one element,component, region or section from another element, component, region orsection. Thus, a first element, component, region or section discussedherein could be termed a second element, component, region or sectionwithout departing from the teachings of the present invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” or “includes” and/or “including,” when used in thisspecification, specify the presence of stated features, regions,integers, steps, operations, elements and/or components, but do notpreclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components and/or groupsthereof.

Reference throughout this specification to “some embodiments,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in someembodiment,” or “in an embodiment,” in various places throughout thisspecification are not necessarily all referring to the same embodiment.Furthermore, the particular features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

1. (canceled)
 2. A computer-implemented method for improving imagequality with shortened acquisition time, the method comprising: (a)receiving a medical image, wherein the medical image is acquired with ashortened acquisition time; (b) applying a deep network model to themedical image to output a predicted medical image, wherein the predictedmedical image has a quality higher than a quality of the medical image,wherein the deep network model is trained using training datasetscomprising at least a pair of high quality image and low quality imageand by adaptively tuning one or more model parameters to estimate afunction that transforms the low quality image to the high qualityimage.
 3. The computer-implemented method of claim 2, wherein thetraining datasets further comprise augmented datasets.
 4. Thecomputer-implemented method of claim 3, wherein the augmented datasetscomprise simulated low quality image.
 5. The computer-implemented methodof claim 4, wherein the simulated low quality image is generated byapplying a filter to or adding noise to a raw image data.
 6. Thecomputer-implemented method of claim 2, wherein the training datasetscomprise image patches that comprise a portion of at least the lowquality image and the high quality image.
 7. The computer-implementedmethod of claim 2, wherein the function is a residual function.
 8. Thecomputer-implemented method of claim 2, wherein the deep network modelis trained using a residual learning framework.
 9. Thecomputer-implemented method of claim 2, wherein the quality of thepredicted medical image has greater signal-to-noise ratio (SNR), higherresolution, or less aliasing compared with the quality of the medicalimage.
 10. The computer-implemented method of claim 2, wherein theshortened acquisition time is achieved by undersampling k-space,reducing number of repetitions or reducing resolution.
 11. Thecomputer-implemented method of claim 2, wherein the shortenedacquisition time is at least 1.5 times, 2 times, 3 times, 4 times, 5times faster than a standard acquisition time.
 12. A non-transitorycomputer-readable storage medium including instructions that, whenexecuted by one or more processors, cause the one or more processors toperform operations comprising: (a) receiving a medical image, whereinthe medical image is acquired with a shortened acquisition time; (b)applying a deep network model to the medical image to output a predictedmedical image, wherein the predicted medical image has a quality higherthan a quality of the medical image, wherein the deep network model istrained using training datasets comprising at least a pair of highquality image and low quality image and by adaptively tuning one or moremodel parameters to estimate a function that transforms the low qualityimage to the high quality image.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein the trainingdatasets further comprise augmented datasets.
 14. The non-transitorycomputer-readable storage medium of claim 13, wherein the augmenteddatasets comprise simulated low quality image.
 15. The non-transitorycomputer-readable storage medium of claim 14, wherein the simulated lowquality image is generated by applying a filter to or adding noise to araw image data.
 16. The non-transitory computer-readable storage mediumof claim 12, wherein the training datasets comprise image patches thatcomprise a portion of at least the low quality image and the highquality image.
 17. The non-transitory computer-readable storage mediumof claim 12, wherein the function is a residual function.
 18. Thenon-transitory computer-readable storage medium of claim 12, wherein thedeep network model is trained using a residual learning framework. 19.The non-transitory computer-readable storage medium of claim 12, whereinthe quality of the predicted medical image has greater signal-to-noiseratio (SNR), higher resolution, or less aliasing compared with thequality of the medical image.
 20. The non-transitory computer-readablestorage medium of claim 12, wherein the shortened acquisition time isachieved by undersampling k-space, reducing number of repetitions orreducing resolution.
 21. The non-transitory computer-readable storagemedium of claim 12, wherein the shortened acquisition time is at least1.5 times, 2 times, 3 times, 4 times, 5 times faster than a standardacquisition time.